A comprehensive collection of AI/ML tools, frameworks, and resources in the Kubernetes ecosystem for building, deploying, and managing machine learning workloads at scale.

• Overview
• MLOps Platforms
• Model Serving & Inference
• Workflow Orchestration
• Training & Experimentation
• Data Processing & Pipelines
• Monitoring & Observability
• GPU & Resource Management
• Development & Notebooks
• ? MCP Servers for Kubernetes
• Security & Compliance
• Commercial & Managed Solutions
• Getting Started
• Contributing
• Community

Kubernetes has become the de facto standard for orchestrating AI/ML workloads, providing scalability, portability, and robust resource management for machine learning operations. This repository catalogs the essential tools and frameworks that power the Kubernetes AI ecosystem.

• Scalability: Dynamic scaling of ML workloads based on demand
• Portability: Deploy anywhere Kubernetes runs (cloud, on-premise, edge)
• Resource Management: Efficient GPU/CPU allocation and optimization
• Containerization: Consistent environments from development to production
• Automation: GitOps and CI/CD integration for ML pipelines

Comprehensive platforms for end-to-end machine learning lifecycle management.

Tools for deploying and serving ML models in production environments.

Tools for managing and orchestrating ML pipelines and workflows.

Frameworks and tools for distributed training and hyperparameter optimization.

Tools for data ingestion, processing, and pipeline management.

Tools for monitoring ML models and infrastructure performance.

Specialized tools for GPU scheduling and resource optimization.

Interactive development environments and notebook platforms.

Model Context Protocol (MCP) servers enable AI assistants to interact with Kubernetes clusters through standardized interfaces. ? View Complete MCP Servers Guide

# Install the recommended MCP server
npx kubernetes-mcp-server@latest

# Add to Claude Desktop config
{
  "mcpServers": {
    "kubernetes": {
      "command": "npx",
      "args": ["kubernetes-mcp-server@latest"]
    }
  }
}

Use Cases:

• ?? Natural language cluster management
• ? Automated troubleshooting with AI
• ? Resource discovery and analysis
• ? Security auditing assistance

Tools and frameworks for securing ML workloads and ensuring compliance.

Enterprise and cloud-managed platforms for Kubernetes AI/ML.

• Kubernetes cluster (v1.20+)
• kubectl configured
• Basic understanding of containers and Kubernetes
• Python/R for ML development

# Install kfctl
wget https://github.com/kubeflow/kfctl/releases/download/v1.2.0/kfctl_v1.2.0_linux.tar.gz
tar -xvf kfctl_v1.2.0_linux.tar.gz
sudo mv kfctl /usr/local/bin/

# Deploy Kubeflow
export KF_NAME=my-kubeflow
export BASE_DIR=${HOME}/kubeflow
export KF_DIR=${BASE_DIR}/${KF_NAME}
export CONFIG_URI="https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_k8s_istio.v1.2.0.yaml"

mkdir -p ${KF_DIR}
cd ${KF_DIR}
kfctl apply -V -f ${CONFIG_URI}

kubectl apply -f https://github.com/kserve/kserve/releases/download/v0.8.0/kserve.yaml

kubectl create namespace argo
kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/download/v3.4.4/install.yaml

npx kubernetes-mcp-server@latest

• CNCF AI/ML Working Group
• Kubernetes AI/ML Best Practices
• MLOps Principles
• Model Context Protocol Documentation
• Quick Start Guide

• KubeCon + CloudNativeCon
• MLOps World
• Kubeflow Community

We welcome contributions! Please read our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/add-new-tool)
3. Add your changes in the appropriate category table
4. Commit your changes (git commit -am 'Add new ML tool')
5. Push to the branch (git push origin feature/add-new-tool)
6. Create a Pull Request

Join our community to discuss Kubernetes AI/ML topics:

• Slack: Collabnix Community
• Twitter: @collabnix
• Blog: Collabnix.com
• YouTube: Collabnix Channel

This project is licensed under the MIT License - see the LICENSE file for details.

• Thanks to all the open-source contributors in the Kubernetes and AI/ML communities
• Special recognition to CNCF projects that power cloud-native AI/ML
• Inspired by the Cloud Native Landscape project
• MCP servers community for advancing AI-infrastructure integration

Maintained by: Collabnix Community Last Updated: May 2025

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